Drivers of satisfaction and dissatisfaction for overseas service consumers : a critical incident technique approach

This is one of the few studies in the academic literature that directly addresses inward exporting of customer services, which is a topic that has gained less attention from an international services marketing point of view. The objective of this study is to explore the drivers of satisfaction and dissatisfaction for overseas service customers of higher education in Australia. Critical incident technique (CIT) method was used to collect and analyse the data and a total of 107 critical incidents were collected. Findings from this study show that service satisfaction and dissatisfaction for international students derive from: elements of the core service (educational service performance), personal sources (international student performance), and the external environment (socialization and host environment performance). Additionally, results show that the drivers of satisfaction and dissatisfaction for international students are not necessarily the same. Limitations relating to the specific sector of higher education and the cross sectional natures of the data are addressed.

Inward internationalization of services : Exploring satisfaction and dissatisfaction of overseas students in Australia

Most research has assessed the outward internationalization process of service firms and less is known in the literature about the inward internationalization of services, or companies that provide the service to overseas customers in the domestic market (i.e., tourism, education, healthcare). Specifically, there is scant research looking at the overseas customer perspective. This study attempts to identify the main drivers of satisfaction and dissatisfaction for overseas customers. The critical incident technique (CIT) method was used to collect and analyse the data. A total of 107 critical incidents regarding drivers of satisfaction and dissatisfaction for overseas customers in a higher educational service context were collected. Findings of this study show that drivers of service satisfaction and dissatisfaction for overseas customers derive from elements of the core service and peripheral service. However, the findings show that elements of the peripheral service (living environment, socialization and interaction with others, and personal performance) are more important for international students.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Proximity-to-goal as a constraint on patterns of behaviour in attacker–defender dyads in team games

The aim of this study was to determine whether spatiotemporal interactions between footballers and the ball in 1 vs. 1 sub-phases are influenced by their proximity to the goal area. Twelve participants (age 15.3 ± 0.5 years) performed as attackers and defenders in 1 vs. 1 dyads across three field positions: (a) attacking the goal, (b) in midfield, and (c) advancing away from the goal area. In each position, the dribbler was required to move beyond an immediate defender with the ball towards the opposition goal. Interactions of attacker-defender dyads were filmed with player and ball displacement trajectories digitized using manual tracking software. One-way repeated measures analysis of variance was used to examine differences in mean defender-to-ball distance after this value had stabilized. Maximum attacker-to-ball distance was also compared as a function of proximity-to-goal. Significant differences were observed for defender-to-ball distance between locations (a) and (c) at the moment when the defender-to-ball distance had stabilized (a: 1.69 ± 0.64 m; c: 1.15 ± 0.59 m; P < 0.05). Findings indicate that proximity-to-goal influenced the performance of players, particularly when attacking or advancing away from goal areas, providing implications for training design in football. In this study, the task constraints of football revealed subtly different player interactions than observed in previous studies of dyadic systems in basketball and rugby union.

Developing a fuzzy multi-criteria decision-making model for selecting design-build operational variations

Many academic researchers have conducted studies on the selection of design-build (DB) delivery method; however, there are few studies on the selection of DB operational variations, which poses challenges to many clients. The selection of DB operational variation is a multi-criteria decision making process that requires clients to objectively evaluate the performance of each DB operational variation with reference to the selection criteria. This evaluation process is often characterized by subjectivity and uncertainty. In order to resolve this deficiency, the current investigation aimed to establish a fuzzy multicriteria decision-making (FMCDM) model for selecting the most suitable DB operational variation. A three-round Delphi questionnaire survey was conducted to identify the selection criteria and their relative importance. A fuzzy set theory approach, namely the modified horizontal approach with the bisector error method, was applied to establish the fuzzy membership functions, which enables clients to perform quantitative calculations on the performance of each DB operational variation. The FMCDM was developed using the weighted mean method to aggregate the overall performance of DB operational variations with regard to the selection criteria. The proposed FMCDM model enables clients to perform quantitative calculations in a fuzzy decision-making environment and provides a useful tool to cope with different project attributes.

From frustration to satisfaction : using NLP to improve self-expression

How often do students tell us they are frustrated at being unable to express themselves, and more specifically, their true, deep and complex thoughts? We reassure them that language learning takes time, and that, with concerted effort, they will learn English. And mostly they do, but being able to fulfil various forms of academic assessment does not necessarily mean that non-native speakers can express, to their complete satisfaction, the depth and subtleties of their true thoughts and feelings such as is possible in their own language. Neuro-linguistic programming (NLP) is making an impact on English language teaching, and may just offer one solution to this problem. By drawing upon the notion of preferred representational systems, this paper suggests that expressing oneself with satisfaction may be as simple as understanding how one processes and stores information.

Predicting the job satisfaction of female sex workers in Queensland, Australia

This study used a cross-sectional survey to examine job satisfaction and its correlates among 247 female sex workers working as private service providers, in licensed brothels and in illegal sectors of the industry (mainly street-based workers). Overall, most sex workers reported positive job satisfaction. Satisfaction was higher in women working legally and was generally comparable with women from the general population. Multivariate analyses revealed that job satisfaction was significantly linked to women’s reasons for initially entering the industry. Sex workers’ age, education, marital status, length of time in the industry and current working conditions were apparently less important for satisfaction.

Development of distributed fuzzy systems with a runtime-adaptable mobile components framework

A distributed fuzzy system is a real-time fuzzy system in which the input, output and computation may be located on different networked computing nodes. The ability for a distributed software application, such as a distributed fuzzy system, to adapt to changes in the computing network at runtime can provide real-time performance improvement and fault-tolerance. This paper introduces an Adaptable Mobile Component Framework (AMCF) that provides a distributed dataflow-based platform with a fine-grained level of runtime reconfigurability. The execution location of small fragments (possibly as little as few machine-code instructions) of an AMCF application can be moved between different computing nodes at runtime. A case study is included that demonstrates the applicability of the AMCF to a distributed fuzzy system scenario involving multiple physical agents (such as autonomous robots). Using the AMCF, fuzzy systems can now be developed such that they can be distributed automatically across multiple computing nodes and are adaptable to runtime changes in the networked computing environment. This provides the opportunity to improve the performance of fuzzy systems deployed in scenarios where the computing environment is resource-constrained and volatile, such as multiple autonomous robots, smart environments and sensor networks.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of cross- entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

UAS See-and-Avoid using two different approaches of Fuzzy Control

This work presents two UAS See and Avoid approaches using Fuzzy Control. We compare the performance of each controller when a Cross-Entropy method is applied to optimase the parameters for one of the controllers. Each controller receive information from an image processing front-end that detect and track targets in the environment. Visual information is then used under a visual servoing approach to perform autonomous avoidance. Experimental flight trials using a small quadrotor were performed to validate and compare the behaviour of both controllers

Job satisfaction and intention to leave among critical care nurses in Saudi Arabia

Aim The purpose of this study was to examine the relationship between registered nurses’ (RN) job satisfaction and their intention to leave critical care nursing in Saudi Arabia. Background Many studies have identified critical care areas as stressful work environments for nurses and have identified factors contributing to job satisfaction and staff retention. However, very little research has examined these relationships in the Saudi context. Design and Methods This study utilised an exploratory, cross-sectional survey design to examine the relationship between RN job satisfaction and intention to leave at King Abdul-Aziz University Hospital, Saudi Arabia. Respondents completed a self-administered survey including demographic items and validated measures of job satisfaction and intention to leave. A convenience sample of 182 RNs working in critical care areas during the data collection period were included. Results Regression analysis predicting RN intention to leave found that demographic variables including age, parental status and length of ICU experience, and three of the job satisfaction subscales including perceived workload, professional support and pay and prospects for promotion, were significantly associated with the outcome variable. Conclusion This study adds to the existing literature on the relationship between job satisfaction and intention to leave critical care areas among RNs working in Saudi Arabia. These findings point to the need for management and policy interventions targeting nurses’ workloads, professional support and pay and promotion in order to improve nurse retention.

Intuitionistic fuzzy Choquet integral operator-based approach for black-start decision-making

Determining the optimal of black-start strategies is very important for speeding the restoration speed of a power system after a global blackout. Most existing black-start decision-making methods are based on the assumption that all indexes are independent of each other, and little attention has been paid to the group decision-making method which is more reliable. Given this background, the intuitionistic fuzzy set and further intuitionistic fuzzy Choquet integral operator are presented, and a black-start decision-making method based on this integral operator is presented. Compared to existing methods, the proposed algorithm cannot only deal with the relevance among the indexes, but also overcome some shortcomings of the existing methods. Finally, an example is used to demonstrate the proposed method. © 2012 The Institution of Engineering and Technology.

Patients’ satisfaction with acute care hospital foodservice: does age matter?

In an aging population, healthcare providers should understand the foodservice preferences of the elderly to reduce the risk of malnutrition through adequate nutrition. Conflicting reports exist for elderly patient satisfaction regarding foodservice.1 This study aimed to investigate the relationship between age and foodservice satisfaction within the acute care hospital setting. Patient satisfaction was assessed using the Acute Care Hospital Foodservice Patient Satisfaction Questionnaire with data collected over three years (2008–2010, n = 785) at The Wesley Hospital, Brisbane. Age was grouped into three categories; <50, 51–70 and >70 years. ANOVA was used to measure age related differences in patients’ overall foodservice satisfaction, four foodservice dimensions and two independent statements (meal size and hot food temperature). Results showed that older patients were more satisfied than younger patients and indicated increasing satisfaction with increasing age regarding food quality (F2,767 = 15.787, p < 0.001), staff/service issues (F2,768 = 12.243, p < 0.001), physical environment (F2,765 = 5.454, p = 0.004), meal size (F2,730 = 10.646, p < 0.001) and hot food temperature (F2,730 = 10.646, p < 0.001). While patients aged >70 years also reported greater satisfaction with meal service quality, those aged 51–70 years indicated the lowest (F2,762 = 9.988, p < 0.001). Overall patient satisfaction across all age groups was high (4.26–4.43/5) and a trend of increasing satisfaction with increasing age was evident (F2,752 = 2.900, p = 0.056). These findings suggest patients’ satisfaction with hospital foodservice increases with age and can assist foodservices to meet the varying generational expectations of their clients.